Vanishing Gradients

While many people talk about “agents,” Shreya Shankar (UC Berkeley) has been building the systems that make them reliable. In this episode, she shares how AI agents and LLM judges can be used to proc…

While much of the AI world chases ever-larger models, Ravin Kumar (Google DeepMind) and his team build across the size spectrum, from billions of parameters down to this week’s release: Gemma 270M, t…

Traditional software expects 100% passing tests. In LLM-powered systems, that’s not just unrealistic — it’s a feature, not a bug. Eric Ma leads research data science in Moderna’s data science and AI …

Colab is cozy. But production won’t fit on a single GPU.
Zach Mueller leads Accelerate at Hugging Face and spends his days helping people go from solo scripts to scalable systems. In this episode, he…

Demos are easy; durability is hard. Samuel Colvin has spent a decade building guardrails in Python (first with Pydantic, now with Logfire), and he’s convinced most LLM failures have nothing to do wit…

Most LLM-powered features do not break at the model. They break at the context. So how do you retrieve the right information to get useful results, even under vague or messy user queries?

In this ep…

What does it take to actually ship LLM-powered features, and what breaks when you connect them to real production data?

In this episode, we hear from Philip Carter — then a Principal PM at Honeycomb…

If we want AI systems that actually work, we need to get much better at evaluating them, not just building more pipelines, agents, and frameworks.

In this episode, Hugo talks with Hamel Hussain (ex-…

If we want AI systems that actually work in production, we need better infrastructure—not just better models.

In this episode, Hugo talks with Akshay Agrawal (Marimo, ex-Google Brain, Netflix, Stanf…

If we want to make progress toward AGI, we need a clear definition of intelligence—and a way to measure it.

In this episode, Hugo talks with Greg Kamradt, President of the ARC Prize Foundation, abou…

What if the cost of writing code dropped to zero — but the cost of understanding it skyrocketed?

In this episode, Hugo sits down with Joe Reis to unpack how AI tooling is reshaping the software deve…

What if building software felt more like composing than coding?

In this episode, Hugo and Greg explore how LLMs are reshaping the way we think about software development—from deterministic programmi…

Too many teams are building AI applications without truly understanding why their models fail. Instead of jumping straight to LLM evaluations, dashboards, or vibe checks, how do you actually fix a br…

AI coding assistants are reshaping how developers write, debug, and maintain code—but who’s really in control? In this episode, Hugo speaks with Tyler Dunn, CEO and co-founder of Continue, an open-so…

Hugo speaks with Alex Strick van Linschoten, Machine Learning Engineer at ZenML and creator of a comprehensive LLMOps database documenting over 400 deployments. Alex's extensive research into real-wo…

In this episode of Vanishing Gradients, the tables turn as Hugo sits down with Alex Andorra, host of Learning Bayesian Statistics. Hugo shares his journey from mathematics to AI, reflecting on how Ba…

Hugo Bowne-Anderson hosts a panel discussion from the MLOps World and Generative AI Summit in Austin, exploring the long-term growth of AI by distinguishing real problem-solving from trend-based solu…

Hugo speaks with Charles Frye, Developer Advocate at Modal and someone who really knows GPUs inside and out. If you’re a data scientist, machine learning engineer, AI researcher, or just someone tryi…

Hugo speaks with Ravin Kumar,*Senior Research Data Scientist at Google Labs. Ravin’s career has taken him from building rockets at SpaceX to driving data science and technology at Sweetgreen, and now…

Hugo speaks with Jason Liu, an independent AI consultant with experience at Meta and Stitch Fix. At Stitch Fix, Jason developed impactful AI systems, like a $50 million product similarity search and …